1. Field of the Invention
The present invention relates to the field of computer vision and image and gesture recognition. More specifically, this invention relates to a stereo-based approach for gesture recognition.
2. Background
Gestures enrich human communication in a natural and unobtrusive way. Gestures can also enrich human-computer interaction in a similar way. Gestures can provide part of a more natural human computer interface. As in the past, when decreasing cost and increasing computer power enabled development of graphical interfaces controlled by 2D mouse movements, so too today, computers and processors, and other technology are becoming powerful enough to provide a new avenue of human-computer communication and interaction.
Gesture modeling can be classified as either model-based or appearance-based. Model-based approaches for moving articulated objects such as human hands are computationally expensive and difficult to implement. Conversely, appearance-based models need to take account of changing views and topology. Vision-based gesture recognition has been applied successfully to numerous applications such as computer game navigation, TV remote control, American Sign Language recognition, and virtual navigation. The recent availability of stereo camera systems has stimulated new research on stereo-based tracking. Some recent systems use stereo cameras to track self-occluding articulated objects or to combine stereo with other cues to track people. One prior art system presents a method for fitting a complex articulated model of the hand to stereo data. Another prior art system combines range, edge, and color information to track fingers. Image unwarping based on model orientation has been used in the field of face recognition and has, up to now not been applied in the field of hand gesture recognition using stereo images.
Much of the previous work in gesture recognition excludes stereo images. The prior art includes use of gradient orientation histograms for recognizing hand gestures under constrained movement conditions. Another technique combines gradient histograms with a color probability density tracker to allow hand gesture recognition with free movement. And another method uses a downward pointing camera mounted in a baseball cap to aid in color segmentation of the user""s hands for American Sign Language recognition.
Visual interpretation of human gestures by computers promises a simple and unobtrusive method of human-computer interaction. Yet, a system that can distinguish human gestures in a variety of settings regardless of environmental interference has been elusive.